

import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__snake_case				:      List[Any]   		=			logging.get_logger("""transformers.models.encodec""")

__snake_case				:      Union[str, Any]   		=			{
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
__snake_case				:      Union[str, Any]   		=			{
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
__snake_case				:      Optional[int]   		=			{
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
__snake_case				:      Union[str, Any]   		=			{
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
__snake_case				:      Dict   		=			{
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
__snake_case				:      Union[str, Any]   		=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__snake_case				:      Optional[Any]   		=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__snake_case				:      List[Any]   		=			[]
__snake_case				:      List[str]   		=			[]


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			for attribute in key.split("""."""):
						a_						:						List[str] 							= getattr(a__  ,	a__)

			if weight_type is not None:
						a_						:						Union[str, Any] 							= getattr(a__  ,	a__).shape
			else:
						a_						:						List[Any] 							= hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
						    f''' {value.shape} for {full_name}''')

			if weight_type == "weight":
						a_						:						List[Any] 							= value
			elif weight_type == "weight_g":
						a_						:						str 							= value
			elif weight_type == "weight_v":
						a_						:						List[Any] 							= value
			elif weight_type == "bias":
						a_						:						Union[str, Any] 							= value
			elif weight_type == "running_mean":
						a_						:						Tuple 							= value
			elif weight_type == "running_var":
						a_						:						Optional[Any] 							= value
			elif weight_type == "num_batches_tracked":
						a_						:						Tuple 							= value
			elif weight_type == "weight_ih_l0":
						a_						:						Union[str, Any] 							= value
			elif weight_type == "weight_hh_l0":
						a_						:						Tuple 							= value
			elif weight_type == "bias_ih_l0":
						a_						:						Tuple 							= value
			elif weight_type == "bias_hh_l0":
						a_						:						Optional[Any] 							= value
			elif weight_type == "weight_ih_l1":
						a_						:						List[str] 							= value
			elif weight_type == "weight_hh_l1":
						a_						:						Union[str, Any] 							= value
			elif weight_type == "bias_ih_l1":
						a_						:						int 							= value
			elif weight_type == "bias_hh_l1":
						a_						:						List[Any] 							= value
			else:
						a_						:						List[Any] 							= value

			logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.''')


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for key in ignore_keys:
						if key.endswith(""".*"""):
									if name.startswith(key[:-1]):
												return True
						elif ".*." in key:
									a_							,					a_						:						int 							= key.split(""".*.""")
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Dict 							= []

			if model_name == "encodec_24khz" or "encodec_32khz":
						a_						:						List[Any] 							= MAPPING_24K
			elif model_name == "encodec_48khz":
						a_						:						Optional[Any] 							= MAPPING_48K
			else:
						raise ValueError(f'''Unsupported model: {model_name}''')

			for name, value in orig_dict.items():
						if should_ignore(a__  ,	a__):
									logger.info(f'''{name} was ignored''')
									continue

						a_						:						List[str] 							= False
						for key, mapped_key in MAPPING.items():
									if "*" in key:
												a_							,					a_						:						Optional[int] 							= key.split(""".*.""")
												if prefix in name and suffix in name:
															a_						:						Optional[Any] 							= suffix

									if key in name:
												# HACK otherwise .embed gets initialized with .embed_avg too
												if key.endswith("""embed""") and name.endswith("""embed_avg"""):
															continue

												a_						:						Optional[int] 							= True
												if "*" in mapped_key:
															a_						:						str 							= name.split(a__)[0].split(""".""")[-2]
															a_						:						Any 							= mapped_key.replace("""*"""  ,	a__)
												if "weight_g" in name:
															a_						:						Any 							= """weight_g"""
												elif "weight_v" in name:
															a_						:						Tuple 							= """weight_v"""
												elif "weight_ih_l0" in name:
															a_						:						Optional[Any] 							= """weight_ih_l0"""
												elif "weight_hh_l0" in name:
															a_						:						List[Any] 							= """weight_hh_l0"""
												elif "bias_ih_l0" in name:
															a_						:						int 							= """bias_ih_l0"""
												elif "bias_hh_l0" in name:
															a_						:						Dict 							= """bias_hh_l0"""
												elif "weight_ih_l1" in name:
															a_						:						Optional[int] 							= """weight_ih_l1"""
												elif "weight_hh_l1" in name:
															a_						:						Optional[int] 							= """weight_hh_l1"""
												elif "bias_ih_l1" in name:
															a_						:						Union[str, Any] 							= """bias_ih_l1"""
												elif "bias_hh_l1" in name:
															a_						:						Tuple 							= """bias_hh_l1"""
												elif "bias" in name:
															a_						:						Union[str, Any] 							= """bias"""
												elif "weight" in name:
															a_						:						Optional[Any] 							= """weight"""
												elif "running_mean" in name:
															a_						:						Tuple 							= """running_mean"""
												elif "running_var" in name:
															a_						:						Any 							= """running_var"""
												elif "num_batches_tracked" in name:
															a_						:						Optional[Any] 							= """num_batches_tracked"""
												else:
															a_						:						List[Any] 							= None
												set_recursively(a__  ,	a__  ,	a__  ,	a__  ,	a__)
									continue
						if not is_used:
									unused_weights.append(a__)

			logger.warning(f'''Unused weights: {unused_weights}''')


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__=None  ,	a__=None  ,	):

			'''simple docstring'''



			if config_path is not None:
						a_						:						Any 							= EncodecConfig.from_pretrained(a__)
			else:
						a_						:						int 							= EncodecConfig()

			if model_name == "encodec_24khz":
						pass  # config is already correct
			elif model_name == "encodec_32khz":
						a_						:						Optional[int] 							= [8, 5, 4, 4]
						a_						:						Optional[Any] 							= [2.2]
						a_						:						Optional[int] 							= 6_4
						a_						:						str 							= 3_2_0_0_0
						a_						:						str 							= 2_0_4_8
						a_						:						int 							= False
						a_						:						int 							= False
						a_						:						Dict 							= False
			elif model_name == "encodec_48khz":
						a_						:						Tuple 							= [8, 5, 4, 2]
						a_						:						Dict 							= [3.0, 6.0, 12.0, 24.0]
						a_						:						List[str] 							= 4_8_0_0_0
						a_						:						List[Any] 							= 2
						a_						:						Union[str, Any] 							= False
						a_						:						int 							= """time_group_norm"""
						a_						:						Tuple 							= True
						a_						:						Tuple 							= 1.0
						a_						:						int 							= 0.01
			else:
						raise ValueError(f'''Unknown model name: {model_name}''')

			a_						:						Union[str, Any] 							= EncodecModel(a__)

			a_						:						Optional[Any] 							= EncodecFeatureExtractor(
			    feature_size=config.audio_channels  ,	sampling_rate=config.sampling_rate  ,	chunk_length_s=config.chunk_length_s  ,	overlap=config.overlap  ,	)
			feature_extractor.save_pretrained(a__)

			a_						:						Optional[int] 							= torch.load(a__)
			if "best_state" in original_checkpoint:
						# we might have a training state saved, in which case discard the yaml results and just retain the weights
						a_						:						int 							= original_checkpoint["""best_state"""]
			recursively_load_weights(a__  ,	a__  ,	a__)
			model.save_pretrained(a__)

			if repo_id:
						print("""Pushing to the hub...""")
						feature_extractor.push_to_hub(a__)
						model.push_to_hub(a__)


if __name__ == "__main__":
	__snake_case				:      Optional[Any]   		=			argparse.ArgumentParser()
	parser.add_argument(
	    """--model""",
	    default="""encodec_24khz""",
	    type=str,
	    help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
	)
	parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
	)

	__snake_case				:      Any   		=			parser.parse_args()
	convert_checkpoint(
	    args.model,
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.push_to_hub,
	)





import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=99  ,      _lowercase=13  ,      _lowercase=16  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=2  ,      _lowercase=32  ,      _lowercase=4  ,      _lowercase=4  ,      _lowercase=30  ,      _lowercase=0  ,      _lowercase=1  ,      _lowercase=2  ,      _lowercase=None  ,      )     -> List[str]:
									a_						:						Union[str, Any] 							= parent
									a_						:						Dict 							= batch_size
									a_						:						Any 							= decoder_seq_length
									# For common tests
									a_						:						Any 							= self.decoder_seq_length
									a_						:						int 							= is_training
									a_						:						List[Any] 							= use_attention_mask
									a_						:						List[str] 							= use_labels

									a_						:						Optional[Any] 							= vocab_size
									a_						:						Dict 							= d_model
									a_						:						Any 							= d_model
									a_						:						List[Any] 							= decoder_layers
									a_						:						List[str] 							= decoder_layers
									a_						:						Union[str, Any] 							= decoder_ffn_dim
									a_						:						Optional[Any] 							= decoder_attention_heads
									a_						:						Optional[Any] 							= decoder_attention_heads
									a_						:						Tuple 							= eos_token_id
									a_						:						Union[str, Any] 							= bos_token_id
									a_						:						Dict 							= pad_token_id
									a_						:						Union[str, Any] 							= decoder_start_token_id
									a_						:						List[Any] 							= use_cache
									a_						:						List[str] 							= max_position_embeddings

									a_						:						int 							= None
									a_						:						Any 							= decoder_seq_length
									a_						:						Union[str, Any] 							= 2
									a_						:						Optional[int] 							= 1





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[int] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      self.vocab_size    )

									a_						:						Optional[int] 							= None
									if self.use_attention_mask:
												a_						:						List[Any] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      vocab_size=2    )

									a_						:						List[Any] 							= None
									if self.use_labels:
												a_						:						Union[str, Any] 							= ids_tensor([self.batch_size, self.decoder_seq_length]  ,      self.vocab_size    )

									a_						:						Any 							= TrOCRConfig(
									    vocab_size=self.vocab_size  ,      d_model=self.d_model  ,      decoder_layers=self.decoder_layers  ,      decoder_ffn_dim=self.decoder_ffn_dim  ,      decoder_attention_heads=self.decoder_attention_heads  ,      eos_token_id=self.eos_token_id  ,      bos_token_id=self.bos_token_id  ,      use_cache=self.use_cache  ,      pad_token_id=self.pad_token_id  ,      decoder_start_token_id=self.decoder_start_token_id  ,      max_position_embeddings=self.max_position_embeddings  ,      )

									return (config, input_ids, attention_mask, lm_labels)





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> List[Any]:
									a_						:						Optional[int] 							= True
									a_						:						List[str] 							= TrOCRDecoder(config=_lowercase    ).to(_lowercase    ).eval()
									a_						:						str 							= input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									a_						:						Dict 							= model(_lowercase  ,      use_cache=_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase    )
									a_						:						Union[str, Any] 							= model(_lowercase  ,      use_cache=_lowercase    )

									self.parent.assertTrue(len(_lowercase    ) == len(_lowercase    )    )
									self.parent.assertTrue(len(_lowercase    ) == len(_lowercase    ) + 1    )

									a_						:						Dict 							= outputs["""past_key_values"""]

									# create hypothetical next token and extent to next_input_ids
									a_						:						List[str] 							= ids_tensor((2, 1)  ,      config.vocab_size - 1    ) + 1

									# append to next input_ids and
									a_						:						int 							= torch.cat([input_ids, next_tokens]  ,      dim=-1    )

									a_						:						Dict 							= model(_lowercase    )["""last_hidden_state"""]
									a_						:						List[str] 							= model(_lowercase  ,      past_key_values=_lowercase    )["""last_hidden_state"""]

									# select random slice
									a_						:						Optional[Any] 							= ids_tensor((1,)  ,      output_from_past.shape[-1]    ).item()
									a_						:						Union[str, Any] 							= output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									a_						:						Any 							= output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						List[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						List[Any] 							= config_and_inputs

									a_						:						str 							= {"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict







@require_torch
class        A__(a_,  a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						_A    :				Optional[int]       					=			(TrOCRForCausalLM,) if is_torch_available() else ()
						_A    :				Tuple       					=			{'''text-generation''': TrOCRForCausalLM} if is_torch_available() else {}
						_A    :				List[str]       					=			True
						_A    :				List[str]       					=			False





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						str 							= TrOCRStandaloneDecoderModelTester(self  ,      is_training=_lowercase    )
									a_						:						str 							= ConfigTester(self  ,      config_class=_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									pass





						def 			UpperCamelCase__					(    self    )     -> int:
									pass





						def 			UpperCamelCase__					(    self    )     -> str:
									pass





						def 			UpperCamelCase__					(    self    )     -> Dict:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						int 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									return





						@unittest.skip("""The model doesn't support left padding"""    )  # and it's not used enough to be worth fixing :)
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									pass


